Login

Proceedings

Find matching any: Reset
Bolton, C
Nišavić , N
Bonnardel, B
Rodriguez, M
Gibberd, M
Büchele, D
Y. Inamasu, R
Huang, S
Blacker, C
Rydahl, P
Bharatiya, P
Bresilla, K
Herppich, W.B
Belsky, C
Huang, W.M
Weersink, A
Fortunato, M
Yang, G
Badua, S
Benavente, J.C
Badarch, L
Ransom, C.J
Duff, H.D
Neupane, S
Hill, C
Drew, P
Wallace, D
Noguchi, N
H, V
Ha, S
Heuer, B
Freeman, M
Roberts, D.C
Add filter to result:
Authors
Tumenjargal, E
Badarch, L
Ham, W
Kwon, H
Tumenjargal, E
Badarch, L
Ham, W
Kwon, H
Shanwad, U.K
Patil, M.B
H, V
B.G , M
R, P
N.L. , R
S, S
Khosla, R
Patil, V.C
Wang, J.M
Li, C.M
Yang, X.M
Huang, W.M
Yang, H.M
Xu, X.M
Han-ya, I
Ishii, K
Noguchi, N
Rasooli Sharabian, V
Mochizuki, R
Han-ya, I
Noguchi, N
Su, B
Ishii, K
Dong , Y
Wang , J
Li , C
Yang, G
Song, X
Huang , W
Chung, S
Kim, K
Huh, Y
Hur, S
Ha, S
Ryu, M
Kim, H
Han, K
Shanwad, U
H, V
N.L., R
Kanannnavar, P.S
Swamy, S
Patil, M.B
Cohen, Y
Alchanatis, V
Heuer, B
Lemcoff, H
Sprintsin, M
Rosen, C
Mulla, D
Nigon, T
Dar, Z
Cohen, A
Levi, A
Brikman, R
Markovits, T
Rud, R
M. Rabello, L
R. D. Pereira, R
C. Lopes, W
Y. Inamasu, R
V. de Sousa, R
Yao, Y
Miao, Y
Huang, S
Gnyp, M.L
Khosla, R
Jiang, R
Bareth, G
Yao, Y
Miao, Y
Huang, S
Gnyp, M.L
Jiang, R
Chen, X
Bareth, G
Rodriguez, M
Civeira, G
Urricariet, S
Muschietti, P
Lavado, R
Benavente, J.C
Cugnasca, C.E
Barros, M.F
Santos, H.P
http://icons.paqinteractive.com/16x16/ac, G
Meyer-Aurich, A
Gandorfer, M
Weersink, A
Wagner, P
McNeill, D
Bishop-Hurley, G.J
Irvine, L
Freeman, M
Bellenguez, R
Morris, E
Clarke, A
Sunley, S
Hill, C
Cranfield, G
Lai, C
Belsky, C
Zude-Sasse, M
Käthner, J
Herppich, W.B
Selbeck, J
Drew, P
Sudduth, K.A
Sadler, E
Huang, S
Miao, Y
Yuan, F
Gnyp, M.L
Yao, Y
Cao, Q
Lenz-Wiedemann, V
Bareth, G
Gebbers, R
Dworak, V
Mahns, B
Weltzien, C
Büchele, D
Gornushkin, I
Mailwald, M
Ostermann, M
Rühlmann, M
Schmid, T
Maiwald, M
Sumpf, B
Rühlmann, J
Bourouah, M
Scheithauer, H
Heil, K
Heggemann, T
Leenen, M
Pätzold, S
Welp, G
Chudy, T
Mizgirev, A
Wagner, P
Beitz, T
Kumke, M
Riebe, D
Kersebaum, C
Wallor, E
Ransom, C.J
Bean, M
Kitchen, N
Camberato, J
Carter, P
Ferguson, R.B
Fernandez, F.G
Franzen, D.W
Laboski, C
Nafziger, E
Sawyer, J
Shanahan, J
Sharda, A
Badua, S
Flippo, D
Ciampitti, I
Griffin, T.W
Roberts, D.C
Brorsen, B.W
Raun, W.R
Solie, J.B
Bresilla, K
Manfrini, L
Boini, A
Perulli, G
Morandi, B
Grappadelli, L.C
Zebarth, B
Goyer, C
Neupane, S
Li, S
Mills, A
Whitney, S
Cambouris, A
Perron, I
Pannell, D
Weersink, A
Gandorfer, M
Bharatiya, P
Kale, M
Cook, S
Lacoste, M
Evans, F
Ridout, M
Gibberd, M
Oberthur, T
Rydahl, P
Jorgensen, R.N
Dyrmann, M
Jensen, N
Sorensen, M.D
Bojer, O.M
Andersen, P
Ransom, C.J
Kitchen, N.R
Camberato, J.J
Carter, P.R
Ferguson, R.B
Fernandez, F.G
Franzen, D.W
Laboski, C.A
Nafziger, E.D
Shanahan, J
Sawyer, J.E
Sharda, A
Badua, S
Ciampitti, I
Strasser, R
Griffin, T.W
Finegan, M
Wallace, D
Taylor, J
Shahar, Y
James, P
Blacker, C
Leese, S
Sanderson, R
Kavanagh, R
Rydahl, P
Boejer, O
Torresen, K
Montull, J.M
Taberner, A
Bückmann, H
Verschwele, A
Rydahl, P
Boejer, O
Jensen, N
Hartmann, B
Jorgensen, R
Soerensen, M
Andersen, P
Paz, L
Nielsen, M.B
Straw, C
Bolton, C
Young, J
Hejl, R
Friell, J
Watkins, E
Maxwell, B.D
Hegedus, P.D
Loewen, S.D
Duff, H.D
Sheppard, J.W
Peerlinck, A.D
Morales, G.L
Bekkerman, A
Capolicchio, J
Mennuti, D
Milani, I
Fortunato, M
Petix, R
Reyes Gonzalez, J
Sunkevic, M
Bonnardel, B
Bonnardel, B
Nišavić , N
Topics
Engineering Technologies and Advances
Guidance, Robotics, Automation, and GPS Systems
Food Security and Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Precision Horticulture
Precision Nutrient Management
Sensor Application in Managing In-season Crop Variability
Sensor Application in Managing In-season Crop Variability
Precision Conservation
Optimizing Farm-level use of Spatial Technologies
Precision Livestock Management
Engineering Technologies and Advances
Emerging Issues in Precision Agriculture (Energy, Biofuels, Climate Change, Standards)
Precision Horticulture
Sensor Application in Managing In-season Crop Variability
Remote Sensing Applications in Precision Agriculture
Precision Nutrient Management
Engineering Technologies and Advances
Remote Sensing for Nitrogen Management
Big Data, Data Mining and Deep Learning
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Site-Specific Nutrient, Lime and Seed Management
Small Holders and Precision Agriculture
On Farm Experimentation with Site-Specific Technologies
Precision Crop Protection
In-Season Nitrogen Management
Education and Outreach in Precision Agriculture
Geospatial Data
Decision Support Systems
Precision Crop Protection
Drainage Optimization and Variable Rate Irrigation
Precision Agriculture and Global Food Security
Small Holders and Precision Agriculture
Industry Sponsors
Type
Poster
Oral
Year
2012
2010
2014
2016
2008
2018
2022
Home » Authors » Results

Authors

Filter results44 paper(s) found.

1. Developing An Active Crop Sensor-based In-season Nitrogen Management Strategy For Rice In Northeast China

  Crop sensor-based in-season N management strategies have been successfully developed and evaluated for winter wheat around the world, but little has been reported for rice. The objective of this study was to develop an active crop sensor-based in-season N management strategy for upland rice in Northeast... Y. Yao, Y. Miao, S. Huang, M.L. Gnyp, R. Jiang, X. Chen, G. Bareth

2. The Application Of Fertilizer Using Management Zone (MZ) In Pampas Soils With Texture Variability Affects Residual Nitrate After Harvest

          The maize yields are usually associated with soil texture heterogeneity in western Argentinean Pampas.  In this area, the uniform fertilizer management (UM) increased the risk of nitrate leaching due to over-fertilizing but it could be minimized by using different management zones criteria (MZ). In a field experiment, the nitrates distribution in soil depth (0-1.80 m) at sowing and harvest times (residual Nitrate) and the maize... M. Rodriguez, G. Civeira, S. Urricariet, P. Muschietti, R. Lavado

3. Changes Of Data Sampling Procedure To Avoid Energy And Data Losses During Microclimates Monitoring With Wireless Sensor Networks

... J.C. Benavente, C.E. Cugnasca, M.F. Barros, H.P. Santos, G. Http://icons.paqinteractive.com/16x16/ac

4. Economic Potential Of Monitoring Protein Content At Harvest And Blending Wheat Grain

  Precision agriculture has been primarily focused on the management of inputs but recently developed technologies that monitor grain quality at harvest create the opportunity to manage outputs spatially.  Provided specific product qualities achieve higher prices, monitoring, separation and blending may be economically justified. This paper analyzes the potential economic effects of blending different grain qualities at the farm level. We estimated sub-field specific... A. Meyer-aurich, M. Gandorfer, A. Weersink, P. Wagner

5. A Preliminary Evaluation Of Proximity Loggers To Detect Oestrus Behaviour In Grazing Dairy Cows

... D. Mcneill, G.J. Bishop-hurley, L. Irvine, M. Freeman, R. Bellenguez

6. Attaching Multiple Conductivity Meters To An Atv To Speed Up Precision Agriculture Soil Surveys

Ground conductivity meters are used in a number of precision agriculture applications, including the estimation of water content, nutrient levels, salinity and depth of topsoil. Typically the Geonics EM38 conductivity meter, and to a lesser extent the EM31, are used for soil surveys. Most conductivity surveys involve towing a ground conductivity meter behind an all-terrain vehicle (ATV). In some situations, such as rutted or sloping fields, it is preferable to mount the conductivity meter directly... E. Morris, A. Clarke, S. Sunley, C. Hill, G. Cranfield

7. Implementation of ECU For Agricultural Machines Based On IsoAgLib Open Source

In this paper work, we consider implementation of electronic control unit (ECU) for agricultural machineries. Software implementation is based on IsoAgLib library developed by OSB&IT Engineering Company. We modify IsoAgLib and upgrade it for our target system. The IsoAgLib is an object oriented C++ library that has the communication services and management systems according to the ISO 11783 standard. This library allows building ISOBUS compatible equipment without the protocols implementation... E. Tumenjargal, L. Badarch, W. Ham, H. Kwon

8. Design and Implementation of Virtual Terminal Based On ISO11783 Standard for Agricultural Tractors

The modern agricultural machinery most common use of the embedded electronic and remote sensing technology demands adoption of the Precision Agriculture (PA). One of the common devices is the Virtual Terminal (VT) for tractor. The VT’s functions and terminology are described in the ISO11783 standard. This work presents the control system design and implementation of the VT and some Electronic Control Units (ECU) for agricultural vehicles based on the ISO 11783 standard. The VT development... E. Tumenjargal, L. Badarch, W. Ham, H. Kwon

9. Precision Agriculture Initiative for Karnataka – A New Direction for Strengthening Farming Community

Strengthening agriculture is crucial to meet the myriad challenges of rural poverty, food security, unemployment, and sustainability of natural resources and it also needs strengthening at technical, financial and management levels. In this context... U.K. Shanwad, M.B. Patil, V. H, M. B.g , P. R, R. N.l. , S. S, R. Khosla, V.C. Patil

10. Estimation of Leaf Nitrogen Concentration in Barley with In Situ Hyperspectral Measurements

Leaf nitrogen concentration (LNC), a good indicator of nitrogen status in crop, is of special significance to diagnose nutrient stress and guide nitrogen fertilization in fields. Due to its non-destructive and quick advantages, hyperspectral remote sensing plays a unique role... J.M. Wang, C.M. Li, X.M. Yang, W.M. Huang, H.M. Yang, X.M. Xu

11. Appropriate Wavelengths for Winter Wheat Growth Status Based On Multi-Spectral Crop Reflectance Data

One of the applications of remote sensing in agriculture is to obtain crop status for estimation and management of variable rate of inputs in the crop production. In order to select the appropriate wavelengths related... I. Han-ya, K. Ishii, N. Noguchi, V. Rasooli sharabian

12. Remote Sensing Imagery Based Agricultural Land Pattern Extraction around Miyajimanuma Wetland

This research aimed to extract agricultural land use pattern around the Miyajimanuma wetland, Hokkaido, Japan. By combining the image segmentation technology - watershed transform and image classification technology- particle swarm optimization (PSO)-k-means based minimum distance classifier, a new method for extracting the agricultural land use information based... R. Mochizuki, I. Han-ya, N. Noguchi, B. Su, K. Ishii

13. Estimating Crop Leaf Area Index from Remotely Sensed Data: Scale Effects and Scaling Methods

Leaf area index (LAI) of crop canopies is significant for growth condition monitoring and crop yield estimation, and estimating LAI based on remote sensing observations is the normal way to assess regional crop growth. However, the scale effects of LAI make multi-scale observations harder to be fully and effectively utilized for LAI estimation. A systematical statistical strategy... Y. Dong , J. Wang , C. Li , G. Yang, X. Song, W. Huang

14. Determination of Sensor Locations for Monitoring of Greenhouse Ambient Environment

In protected crop production facilities such as greenhouse and plant factory, f... S. Chung, K. Kim, Y. Huh, S. Hur, S. Ha, M. Ryu, H. kim, K. han

15. Precision Nutrient Management in Cotton- A Case Study from India

Cotton is being one of the important commercial crops in India, farmers have adopted cultivating hybrid cotton to achieve higher yield. In this context, cotton is becoming input intensive crop... U. Shanwad, V. H, R. N.l., P.S. Kanannnavar, S. Swamy, M.B. Patil

16. Evaluating Water Status in Potato Fields Using Combined Information from RGB and Thermal Aerial Images

Potato yield and quality are highly dependent on an adequate supply of water. In this study the combined information from RGB and thermal aerial images to evaluate... Y. Cohen, V. Alchanatis, B. Heuer, H. Lemcoff, M. Sprintsin, C. Rosen, D. Mulla, T. Nigon, Z. Dar, A. Cohen, A. Levi, R. Brikman, T. Markovits, R. Rud

17. Implementation of a Controller Unit Based on the ISO 11783 Standard for Automatic Measurement of the Electrical Conductivity of the Soil

... L. M. rabello, R. R. d. pereira, W. C. lopes, R. Y. inamasu, R. V. de sousa

18. In-season Diagnosis of Rice Nitrogen Status Using an Active Canopy Sensor

... Y. Yao, Y. Miao, S. Huang, M. Gnyp, R. Khosla, R. Jiang, G. Bareth

19. Building Proactive Predictive Models With Big Data Technology For Precision Agriculture

In a world with ever increasing shortages of food production due to increasing populations and depletion of resources, the need for new technologies and techniques for sustainable and efficient agriculture with long term financial, environmental and cultural benefits are critical.  An area of scientific study concerning crop-production management called Precision Agriculture (PA) is a concept based on integrating modern information technologies such as Big Data Analytics, GPS... C. Lai, C. Belsky

20. Comparison of Plant and Soil Mapping in Prunus Domestica L. Orchard

In the present study, the soil apparent electrical conductivity, ECa, and the plant water status were analyzed in plum production (Prunus domestica L 'Tophit plus'/Wavit) targeting (i) the spatial characterization of soil ECa and fruit yield, (ii) instantaneous water status, and (iii) cumulative pattern of water status and yield. The plum orchard is located in semi-humid, temperate climate (Potsdam, Germany), capturing 0.37 ha with 156 trees. Measurements were carried out on... M. Zude-sasse, J. Käthner, W.B. Herppich, J. Selbeck

21. Development of a Multispectral Sensor for Crop Canopy Temperature Measurement

Quantifying spatial and temporal variability in plant stress has precision agriculture applications in controlling variable rate irrigation and variable rate nutrient application. One approach to plant stress detection is crop canopy temperature measurement by the use of thermographic or radiometric methods, generally in the long wave infrared (LWIR) wavelength range. A confounding factor in LWIR canopy temperature estimation is eliminating the effect of the soil background in the image. One approach... P. Drew, K.A. Sudduth, E. Sadler

22. Potential Improvement in Rice Nitrogen Status Monitoring Using Rapideye and Worldview-2 Satellite Remote Sensing

For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large area in a timely fashion. Satellite remote sensing provides a promising technology for crop growth monitoring and precision management over large areas. The FORMOSAT-2 satellite remote sensing imageries with 4 wavebands have been used to estimate rice N status. The objective of this study was to evaluate the potential of using high spatial resolution... S. Huang, Y. Miao, F. Yuan, M.L. Gnyp, Y. Yao, Q. Cao, V. Lenz-wiedemann, G. Bareth

23. Integrated Approach to Site-specific Soil Fertility Management

In precision agriculture the lack of affordable methods for mapping relevant soil attributes is a funda­mental problem. It restricts the development and application of advanced models and algorithms for decision making. The project “I4S - Integrated System for Site-Specific Soil Fertility Management” combines new sensing technologies with dynamic soil-crop models and decision support systems. Using sensors with different measurement principles improves the estimation of soil fertility... R. Gebbers, V. Dworak, B. Mahns, C. Weltzien, D. Büchele, I. Gornushkin, M. Mailwald, M. Ostermann, M. Rühlmann, T. Schmid, M. Maiwald, B. Sumpf, J. Rühlmann, M. Bourouah, H. Scheithauer, K. Heil, T. Heggemann, M. Leenen, S. Pätzold, G. Welp, T. Chudy, A. Mizgirev, P. Wagner, T. Beitz, M. Kumke, D. Riebe, C. Kersebaum, E. Wallor

24. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor Algorithm

Nitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan

25. Real-time Gauge Wheel Load Variability on Planter with Downforce Control During Field Operation

Downforce control allows planters to maintain gauge wheel load across a range of soil resistance within a field. Downforce control is typically set for a target seed depth and either set to manually or automatically control the gauge wheel load. This technology uses load cells to actively regulate downforce on individual row units by monitoring target load on the gauge wheels. However, no studies have been conducted to evaluate the variability in gauge wheel load observed during planter operation... A. Sharda, S. Badua, D. Flippo, I. Ciampitti, T.W. Griffin

26. Prediction of Nitrogen Needs with Nitrogen-rich Strips and Ramped Nitrogen Strips

Both nitrogen rich strips and ramped nitrogen strips have been used to estimate topdress nitrogen needs for winter wheat based on in-season optical reflectance data. The ramped strip system places a series of small plots in each field with increasing levels of nitrogen to determine the application rate at which predicted yield response to nitrogen reaches a plateau. The nitrogen-rich strip system uses a nitrogen fertilizer optimization algorithm based on optical reflectance measures from the nitrogen-rich... D.C. Roberts, B.W. Brorsen, W.R. Raun, J.B. Solie

27. Using Deep Learning - Convolutional Naural Networks (CNNS) for Real-Time Fruit Detection in the Tree

Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms have shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific... K. Bresilla, L. Manfrini, A. Boini, G. Perulli, B. Morandi, L.C. Grappadelli

28. Soil Microbial Communities Have Distinct Spatial Patterns in Agricultural Fields

Soil microbial communities mediate many important soil processes in agricultural fields, however their spatial distribution at distances relevant to precision agriculture is poorly understood. This study examined the soil physico-chemical properties and topographic features controlling the spatial distribution of soil microbial communities in a commercial potato field in eastern Canada using next generation sequencing. Soil was collected from a transect (1100 m) with 83 sampling points in a landscape... B. Zebarth, C. Goyer, S. Neupane, S. Li, A. Mills, S. Whitney, A. Cambouris, I. Perron

29. Flat Payoff Functions and Site-Specific Crop Management

Within the neighbourhood of any economically “optimal” management system, there is a set of alternative systems that are only slightly less attractive than the optimum. Often this set is large; in other words, the payoff function is flat within the vicinity of the optimum. This has major implications for the economics of variable-rate site-specific crop management. The flatter the payoff function, the lower the benefits of precision in the adjustment of input rates spatially within... D. Pannell, A. Weersink, M. Gandorfer

30. Precision Agriculture for Small Farm Holders

Precision Agriculture is a data-based decision making farming process taking in-field variability into consideration. It uses multiple advance tools and technologies like GPS, GIS, VRT and provides substantial value in terms of minimizing input and maximizing profit to farmers in regions like Canada, North America who have larger land holding capacity. Precision agriculture technologies require significant investment in terms of capital which is most of the time not feasible for farmers with small... P. Bharatiya, M. Kale

31. An On-farm Experimental Philosophy for Farmer-centric Digital Innovation

In this paper, we review learnings gained from early On-Farm Experiments (OFE) conducted in the broadacre Australian grain industry from the 1990s to the present day. Although the initiative was originally centered around the possibilities of new data and analytics in precision agriculture, we discovered that OFEs could represent a platform for engaging farmers around digital technologies and innovation. Insight from interacting closely with farmers and advisors leads us to argue for a change... S. Cook, M. Lacoste, F. Evans, M. Ridout, M. Gibberd, T. Oberthur

32. Spatial Variability of Optimized Herbicide Mixtures and Dosages

Driven by 25 years of Danish, political 'pesticide action plans', aiming at reducing the use of pesticides, a Danish Decision Support System (DSS) for Integrated Weed Management (IWM) has been constructed. This online tool, called ‘IPMwise’ is now in its 4th generation. It integrates the 8 general IPM-principles as defined by the EU. In Denmark, this DSS includes 30 crops, 105 weeds and full assortments of herbicides. Due to generic qualities in both the integrated... P. Rydahl, R.N. Jorgensen, M. Dyrmann, N. Jensen, M.D. Sorensen, O.M. Bojer, P. Andersen

33. Improving Corn Nitrogen Rate Recommendations Through Tool Fusion

 Improving corn (Zea maysL,) nitrogen (N) fertilizer rate recommendation tools can improve farmer’s profits and help mitigate N pollution. One way to improve N recommendation methods is to not rely on a single tool, but to employ two or more tools. Thiscould be thoughtof as “tool fusion”.The objective of this analysis was to improve N management by combining N recommendation tools used for guiding rates for an in-seasonN application. This evaluation was... C.J. Ransom, N.R. Kitchen, J.J. Camberato, P.R. Carter, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J. Shanahan, J.E. Sawyer

34. Influence of Planter Downforce Setting and Ground Speed on Seeding Depth and Plant Spacing Uniformity of Corn

Uniform seed placement improves seed-to-soil contact and requires proper selection of downforce control across varying field conditions. At faster ground speeds, downforce changes and it becomes critical to select the level of planter downforce settings to achieve the desired consistency of seed placement during planting. The objective of this study was to assess the effect of ground speed and downforce setting on seeding depth and plant spacing and to evaluate the relationship of ground speed... A. Sharda, S. Badua, I. Ciampitti, R. Strasser, T.W. Griffin

35. Harness the Power of the Internet to Improve Yield

It’s rare to find a fertile farm or ranch that has complete cellular coverage across the entirety of its property. Because networking options like Wi-Fi are limited by restricted infrastructure in these areas, maintaining a reliable flow of connectivity is difficult. Yet, even if consistent cellular coverage is available, it’s frequently cost prohibitive for farm monitoring. Similarly, alternate wireless devices that require batteries aren’t practical because of high maintenance... M. Finegan, D. Wallace

36. Experiences in the Development of Commercial Web-Based Data Engines to Support UK Growers Within an Industry-Academic Partnership

The lifecycle of Precision Agriculture data begins the moment that the measurement is taken, after which it may pass through each multiple data processes until finally arriving as an output employed back in the production system. This flow can be hindered by the fact that many farm datasets have different spatial resolutions. This makes the process to aggregate or analyse multiple Precision Agriculture layers arduous and time consuming.  Precision Decisions Ltd located in Yorkshire,... J. Taylor, Y. Shahar, P. James, C. Blacker, S. Leese, R. Sanderson, R. Kavanagh

37. Economic Potential of IPMwise – a Generic Decision Support System for Integrated Weed Management in 4 Countries

Reducing use and dependency on pesticides in Denmark has been driven by political action plans since the 1980ies, and a series of nationally funded accompanying R&D programs were completed in the period 1989-2006. One result of these programs was a decision support system (DSS) for integrated weed management. The 4th generation (2016) of the agro-biological models and IT-tools in this DSS, named IPMwise. The concept of IPMwise is to systematically exploit that: occurrence... P. Rydahl, O. Boejer, K. Torresen, J.M. Montull, A. Taberner, H. Bückmann, A. Verschwele

38. Economic Potential of RoboWeedMaps - Use of Deep Learning for Production of Weed Maps and Herbicide Application Maps

In Denmark, a new IPM ‘product chain’ has been constructed, which starts with systematic photographing of fields and ends up with field- or site-specific herbicide application. A special high-speed camera, mounted on an ATV took sufficiently good pictures of small weed plants, while driving up to 50 km/h. Pictures were uploaded to the RoboWeedMaps online platform, where appointed internal- and external persons with agro-botanical experience executed ‘virtual field inspection’... P. Rydahl, O. Boejer, N. Jensen, B. Hartmann, R. Jorgensen, M. Soerensen, P. Andersen, L. Paz, M.B. Nielsen

39. Soil Moisture Variability on Golf Course Fairways Across the United States: an Opportunity for Water Conservation with Precision Irrigation

Fairways account for an average of 11.3 irrigated hectares on each of the 15,000+ golf courses in the US. Annual median water use per hectare on fairways is between ~2,800,000 and 14,000,000 liters, depending on the region. Conventional fairway irrigation relies on visual observation of the turfgrass, followed by secondary considerations of short-term weather forecasts, which oftentimes lead to “blanket” applications to the entire area. The concept of precision irrigation is a strategy... C. Straw, C. Bolton, J. Young, R. Hejl, J. Friell, E. Watkins

40. Decision Support from On-field Precision Experiments

Empirically driven adaptive management in large-scale commodity crop production has become possible with spatially controlled application and sub-field scale crop monitoring technology. Site-specific experimentation is fundamental to an agroecosystem adaptive management (AAM) framework that results in information for growers to make informed decisions about their practices. Crop production and quality response data from combine harvester mounted sensors and internet available remote sensing data... B.D. Maxwell, P.D. Hegedus, S.D. Loewen, H.D. Duff, J.W. Sheppard, A.D. Peerlinck, G.L. Morales, A. Bekkerman

41. Agriculture Machine Guidance Systems: Performance Analysis of Professional GNSS Receivers

GNSS (Global Navigation Satellite Systems) plays nowadays a major role in different civilian activities and is a key technology enabling innovation in different market sectors. For instance, GNSS-enabled solutions are widespread within the Precision Agriculture and, among them, applications in the field of machinery guidance are commonly employed to optimize typical agriculture practices. The scope of this paper is to present the outcomes of the agriculture testing campaign performed,... J. Capolicchio, D. Mennuti, I. Milani, M. Fortunato, R. Petix, J. Reyes gonzalez, M. Sunkevic

42. Farmer Charlie - Low Cost Data Analytics for Farmers Accessible in the Field

Farmer Charlie, a spin-off of AB5 Consulting Ltd, is based on an affordable business model including five elements: a data analytics platform, an agribusiness ecosystem app, capable of connecting with local third-party apps; weather and in field sensors; wi-fi Internet connectivity; and power to the field and farms via solar panels, where necessary. Farmer Charlie brings information to farmers in their own fields, in an easy plug and play solution, affordable to the farmers and addressing their... B. Bonnardel

43. Farmer Charlie - Low Cost Smart Local Data Available to Remote Farmers

Farmer Charlie brings connectivity and information to farmers, who receive tailored agronomic data to improve their agricultural practice. Farmer Charlie is based on on-site sensors through which soil data can be detected, gathered, and processed by a dedicated server. Broadband communication allows farmers to receive real-time, localised information on tablet or mobile phone. Farmer Charlie is a low-cost solution, it can be adapted to various crops and to detect soil humidity, pH, temperature,... B. Bonnardel

44. MDPI - Agriculture and Agronomy Journals

... N. Nišavić